classification of unseen classes of image in open set classification
I have one possible solution for you inspired by real-time face recognition system. It is a similar case to you.

Create embeddings for each class i.e. person or in your case, a class. (Using Siamese network with ArcFace loss)
When a new image comes, take L1 or cosine distance with the embeddings. You will need a threshold above which you create a embedding for the new guy and give it a new class.

One issue you will think at this point of time is how do you do that when have billions of data points. The search for the matching embeddings can then be done using Hierarchical Navigable Small World graphs (see https://github.com/nmslib/hnswlib).
This solution is loosely connected to the plausible techniques you have mentioned here. And if you still  want something in that regard, I shall add the keyword Active Learning.